# SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization

arXiv: https://arxiv.org/abs/2211.11656 · HTML: https://mlsanity.com/paper/2211.11656/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security.
- Builds on 25 of its 46 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Placed in

- LLM safety and security / [Federated unlearning and poisoning](https://mlsanity.com/safety/federated-unlearning-and-poisoning/)

## Builds on (most cited first, 25 of 25)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Train faster, generalize better: Stability of stochastic gradient descent](https://mlsanity.com/paper/1509.01240/) (2015)
- [Certified Data Removal from Machine Learning Models](https://mlsanity.com/paper/1911.03030/) (2019)
- [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019)
- [Descent-to-Delete: Gradient-Based Methods for Machine Unlearning](https://mlsanity.com/paper/2007.02923/) (2020)
- [Approximate Data Deletion from Machine Learning Models](https://mlsanity.com/paper/2002.10077/) (2020)
- [Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations](https://mlsanity.com/paper/2003.02960/) (2020)
- [Adaptive Machine Unlearning](https://mlsanity.com/paper/2106.04378/) (2021)
- [Mixed-Privacy Forgetting in Deep Networks](https://mlsanity.com/paper/2012.13431/) (2020)
- [Understanding Gradient Clipping in Private SGD: A Geometric Perspective](https://mlsanity.com/paper/2006.15429/) (2020)
- [The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining](https://mlsanity.com/paper/2203.07320/) (2022)
- [Federated Unlearning via Class-Discriminative Pruning](https://mlsanity.com/paper/2110.11794/) (2021)
- [Federated Unlearning: How to Efficiently Erase a Client in FL?](https://mlsanity.com/paper/2207.05521/) (2022)
- [Towards Probabilistic Verification of Machine Unlearning](https://mlsanity.com/paper/2003.04247/) (2020)
- [Certifiable Machine Unlearning for Linear Models](https://mlsanity.com/paper/2106.15093/) (2021)
- [Machine Unlearning of Federated Clusters](https://mlsanity.com/paper/2210.16424/) (2022)
- [Online Forgetting Process for Linear Regression Models](https://mlsanity.com/paper/2012.01668/) (2020)
- [Forget-SVGD: Particle-Based Bayesian Federated Unlearning](https://mlsanity.com/paper/2111.12056/) (2021)

## Built on by (most cited first, 18 of 18)

- [A Survey on Federated Unlearning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2310.20448/) (2023)
- [Machine Unlearning: A Comprehensive Survey](https://mlsanity.com/paper/2405.07406/) (2024)
- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions](https://mlsanity.com/paper/2409.09778/) (2024)
- [When to Forget? Complexity Trade-offs in Machine Unlearning](https://mlsanity.com/paper/2502.17323/) (2025)
- [Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2401.08216/) (2024)
- [Privacy-Preserving Federated Unlearning with Certified Client Removal](https://mlsanity.com/paper/2404.09724/) (2024)
- [ConDa: Fast Federated Unlearning with Contribution Dampening](https://mlsanity.com/paper/2410.04144/) (2024)
- [Addressing Out-of-Label Hazard Detection in Dashcam Videos: Insights from the COOOL Challenge](https://mlsanity.com/paper/2501.16037/) (2025)
- [Certified Unlearning in Decentralized Federated Learning](https://mlsanity.com/paper/2601.06436/) (2026)
- [BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning](https://mlsanity.com/paper/2508.15541/) (2025)
- [A Survey on Federated Unlearning: Challenges and Opportunities](https://mlsanity.com/paper/2403.02437/) (2024)
- [Beyond Right to be Forgotten: Managing Heterogeneity Side Effects Through Strategic Incentives](https://mlsanity.com/paper/2410.15045/) (2024)
- [Federated Unlearning Model Recovery in Data with Skewed Label Distributions](https://mlsanity.com/paper/2412.13466/) (2024)
- [Descend or Rewind? Stochastic Gradient Descent Unlearning](https://mlsanity.com/paper/2511.15983/) (2025)
- [Variance-Reduced (varepsilon,δ)-Unlearning using Forget Set Gradients](https://mlsanity.com/paper/2602.14938/) (2026)
- [pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning](https://mlsanity.com/paper/2606.16304/) (2026)
- [When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks](https://mlsanity.com/paper/2607.28829/) (2026)

## Abstract (arXiv)

Machine Unlearning (MU) is an increasingly important topic in machine learning safety, aiming at removing the contribution of a given data point from a training procedure. Federated Unlearning (FU) consists in extending MU to unlearn a given client's contribution from a federated training routine. While several FU methods have been proposed, we currently lack a general approach providing formal unlearning guarantees to the FedAvg routine, while ensuring scalability and generalization beyond the convex assumption on the clients' loss functions. We aim at filling this gap by proposing SIFU (Sequential Informed Federated Unlearning), a new FU method applying to both convex and non-convex optimization regimes. SIFU naturally applies to FedAvg without additional computational cost for the clients and provides formal guarantees on the quality of the unlearning task. We provide a theoretical analysis of the unlearning properties of SIFU, and practically demonstrate its effectiveness as compared to a panel of unlearning methods from the state-of-the-art.